Overview

Dataset statistics

Number of variables21
Number of observations5000
Missing cells1
Missing cells (%)< 0.1%
Total size in memory820.4 KiB
Average record size in memory168.0 B

Variable types

Categorical7
Numeric14

Alerts

state has a high cardinality: 51 distinct valuesHigh cardinality
phone_number has a high cardinality: 5000 distinct valuesHigh cardinality
total_eve_charge has a high cardinality: 1660 distinct valuesHigh cardinality
total_day_minutes is highly overall correlated with total_day_chargeHigh correlation
total_day_charge is highly overall correlated with total_day_minutesHigh correlation
total_night_minutes is highly overall correlated with total_night_chargeHigh correlation
total_night_charge is highly overall correlated with total_night_minutesHigh correlation
total_intl_minutes is highly overall correlated with total_intl_chargeHigh correlation
total_intl_charge is highly overall correlated with total_intl_minutesHigh correlation
intl_plan is highly imbalanced (54.8%)Imbalance
phone_number is uniformly distributedUniform
phone_number has unique valuesUnique
number_vmail_messages has 3678 (73.6%) zerosZeros
number_customer_service_calls has 1023 (20.5%) zerosZeros

Reproduction

Analysis started2023-03-31 20:06:01.047369
Analysis finished2023-03-31 20:06:19.758985
Duration18.71 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

state
Categorical

Distinct51
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
WV
 
158
MN
 
125
AL
 
124
ID
 
119
VA
 
118
Other values (46)
4356 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters10000
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKS
2nd rowOH
3rd rowNJ
4th rowOH
5th rowOK

Common Values

ValueCountFrequency (%)
Other values (51) 5000
100.0%

Length

2023-03-31T16:06:19.848034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wv 158
 
3.2%
mn 125
 
2.5%
al 124
 
2.5%
id 119
 
2.4%
va 118
 
2.4%
oh 116
 
2.3%
tx 116
 
2.3%
wy 115
 
2.3%
ny 114
 
2.3%
or 114
 
2.3%
Other values (41) 3781
75.6%

Most occurring characters

ValueCountFrequency (%)
Other values (24) 10000
100.0%

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Other values (24) 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Latin
ValueCountFrequency (%)
Other values (24) 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (24) 10000
100.0%

account_length
Real number (ℝ)

Distinct218
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.2586
Minimum1
Maximum243
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:19.970885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile35
Q173
median100
Q3127
95-th percentile167
Maximum243
Range242
Interquartile range (IQR)54

Descriptive statistics

Standard deviation39.69455955
Coefficient of variation (CV)0.3959217418
Kurtosis-0.1016210812
Mean100.2586
Median Absolute Deviation (MAD)27
Skewness0.1092911238
Sum501293
Variance1575.658058
MonotonicityNot monotonic
2023-03-31T16:06:20.066394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (218) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

area_code
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
415
2495 
408
1259 
510
1246 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters15000
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row415
2nd row415
3rd row415
4th row408
5th row415

Common Values

ValueCountFrequency (%)
Other values (3) 5000
100.0%

Length

2023-03-31T16:06:20.145962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-31T16:06:20.219788image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
415 2495
49.9%
408 1259
25.2%
510 1246
24.9%

Most occurring characters

ValueCountFrequency (%)
Other values (5) 15000
100.0%

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
Other values (5) 15000
100.0%

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Common
ValueCountFrequency (%)
Other values (5) 15000
100.0%

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (5) 15000
100.0%

phone_number
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct5000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
382-4657
 
1
328-8230
 
1
413-3643
 
1
418-9100
 
1
411-9481
 
1
Other values (4995)
4995 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters45000
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5000 ?
Unique (%)100.0%

Sample

1st row 382-4657
2nd row 371-7191
3rd row 358-1921
4th row 375-9999
5th row 330-6626

Common Values

ValueCountFrequency (%)
Other values (5000) 5000
100.0%

Length

2023-03-31T16:06:20.284772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
382-4657 1
 
< 0.1%
344-9403 1
 
< 0.1%
330-6626 1
 
< 0.1%
391-8027 1
 
< 0.1%
355-9993 1
 
< 0.1%
329-9001 1
 
< 0.1%
335-4719 1
 
< 0.1%
353-3305 1
 
< 0.1%
330-8173 1
 
< 0.1%
363-1107 1
 
< 0.1%
Other values (4990) 4990
99.8%

Most occurring characters

ValueCountFrequency (%)
Other values (12) 45000
100.0%

Most occurring categories

ValueCountFrequency (%)
Other values (3) 45000
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
Other values (10) 35000
100.0%
Space Separator
ValueCountFrequency (%)
No values found.
Dash Punctuation
ValueCountFrequency (%)
No values found.

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Common
ValueCountFrequency (%)
Other values (12) 45000
100.0%

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (12) 45000
100.0%

intl_plan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
no
4527 
yes
473 

Length

Max length4
Median length3
Mean length3.0946
Min length3

Characters and Unicode

Total characters15473
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row no
2nd row no
3rd row no
4th row yes
5th row yes

Common Values

ValueCountFrequency (%)
Other values (2) 5000
100.0%

Length

2023-03-31T16:06:20.354992image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-31T16:06:20.426826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no 4527
90.5%
yes 473
 
9.5%

Most occurring characters

ValueCountFrequency (%)
Other values (6) 15473
100.0%

Most occurring categories

ValueCountFrequency (%)
Other values (2) 15473
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
Other values (5) 10473
100.0%
Space Separator
ValueCountFrequency (%)
No values found.

Most occurring scripts

ValueCountFrequency (%)
Other values (2) 15473
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Other values (5) 10473
100.0%
Common
ValueCountFrequency (%)
No values found.

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (6) 15473
100.0%

voice_mail_plan
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
no
3677 
yes
1323 

Length

Max length4
Median length3
Mean length3.2646
Min length3

Characters and Unicode

Total characters16323
Distinct characters6
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row yes
2nd row yes
3rd row no
4th row no
5th row no

Common Values

ValueCountFrequency (%)
Other values (2) 5000
100.0%

Length

2023-03-31T16:06:20.491735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-31T16:06:20.566681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
no 3677
73.5%
yes 1323
 
26.5%

Most occurring characters

ValueCountFrequency (%)
Other values (6) 16323
100.0%

Most occurring categories

ValueCountFrequency (%)
Other values (2) 16323
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
Other values (5) 11323
100.0%
Space Separator
ValueCountFrequency (%)
No values found.

Most occurring scripts

ValueCountFrequency (%)
Other values (2) 16323
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Other values (5) 11323
100.0%
Common
ValueCountFrequency (%)
No values found.

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (6) 16323
100.0%

number_vmail_messages
Real number (ℝ)

Distinct48
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7552
Minimum0
Maximum52
Zeros3678
Zeros (%)73.6%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:20.640870image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q317
95-th percentile37
Maximum52
Range52
Interquartile range (IQR)17

Descriptive statistics

Standard deviation13.54639339
Coefficient of variation (CV)1.746749715
Kurtosis0.1991271752
Mean7.7552
Median Absolute Deviation (MAD)0
Skewness1.350493197
Sum38776
Variance183.5047739
MonotonicityNot monotonic
2023-03-31T16:06:20.742042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
Other values (48) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_day_minutes
Real number (ℝ)

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.2889
Minimum0
Maximum351.5
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:20.842546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile91.7
Q1143.7
median180.1
Q3216.2
95-th percentile271.105
Maximum351.5
Range351.5
Interquartile range (IQR)72.5

Descriptive statistics

Standard deviation53.89469917
Coefficient of variation (CV)0.2989352044
Kurtosis-0.02129447073
Mean180.2889
Median Absolute Deviation (MAD)36.3
Skewness-0.01173082717
Sum901444.5
Variance2904.638599
MonotonicityNot monotonic
2023-03-31T16:06:20.940571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (1961) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_day_calls
Real number (ℝ)

Distinct123
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.0294
Minimum0
Maximum165
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:21.044782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile133
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.83119742
Coefficient of variation (CV)0.1982536876
Kurtosis0.1785677943
Mean100.0294
Median Absolute Deviation (MAD)13
Skewness-0.08489096367
Sum500147
Variance393.2763909
MonotonicityNot monotonic
2023-03-31T16:06:21.143036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (123) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_day_charge
Real number (ℝ)

Distinct1961
Distinct (%)39.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.649668
Minimum0
Maximum59.76
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:21.246993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.59
Q124.43
median30.62
Q336.75
95-th percentile46.0905
Maximum59.76
Range59.76
Interquartile range (IQR)12.32

Descriptive statistics

Standard deviation9.162068692
Coefficient of variation (CV)0.298928807
Kurtosis-0.02116592527
Mean30.649668
Median Absolute Deviation (MAD)6.17
Skewness-0.01172900707
Sum153248.34
Variance83.94350271
MonotonicityNot monotonic
2023-03-31T16:06:21.344825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (1961) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_eve_minutes
Real number (ℝ)

Distinct1879
Distinct (%)37.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.63656
Minimum0
Maximum363.7
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:21.447423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile118.495
Q1166.375
median201
Q3234.1
95-th percentile283.72
Maximum363.7
Range363.7
Interquartile range (IQR)67.725

Descriptive statistics

Standard deviation50.55130897
Coefficient of variation (CV)0.2519546237
Kurtosis0.05137513056
Mean200.63656
Median Absolute Deviation (MAD)34
Skewness-0.01101769459
Sum1003182.8
Variance2555.434838
MonotonicityNot monotonic
2023-03-31T16:06:21.547048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (1879) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_eve_calls
Real number (ℝ)

Distinct126
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.191
Minimum0
Maximum170
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:21.652133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3114
95-th percentile133
Maximum170
Range170
Interquartile range (IQR)27

Descriptive statistics

Standard deviation19.82649583
Coefficient of variation (CV)0.1978869942
Kurtosis0.1173634027
Mean100.191
Median Absolute Deviation (MAD)13
Skewness-0.02017520328
Sum500955
Variance393.089937
MonotonicityNot monotonic
2023-03-31T16:06:21.749863image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (126) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_eve_charge
Categorical

Distinct1660
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
15.9
 
15
14.25
 
15
16.12
 
14
18.79
 
13
18.96
 
13
Other values (1655)
4930 

Length

Max length5
Median length5
Mean length4.8426
Min length1

Characters and Unicode

Total characters24213
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique520 ?
Unique (%)10.4%

Sample

1st row16.78
2nd row?
3rd row10.3
4th row5.26
5th row12.61

Common Values

ValueCountFrequency (%)
Other values (1660) 5000
100.0%

Length

2023-03-31T16:06:21.844687image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15.9 15
 
0.3%
14.25 15
 
0.3%
16.12 14
 
0.3%
18.79 13
 
0.3%
18.96 13
 
0.3%
16.97 13
 
0.3%
19.41 12
 
0.2%
18.62 11
 
0.2%
17.82 11
 
0.2%
16.18 11
 
0.2%
Other values (1650) 4872
97.4%

Most occurring characters

ValueCountFrequency (%)
Other values (12) 24213
100.0%

Most occurring categories

ValueCountFrequency (%)
Other values (2) 24213
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
Other values (10) 19213
100.0%
Other Punctuation
ValueCountFrequency (%)
Other values (2) 5000
100.0%

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Common
ValueCountFrequency (%)
Other values (12) 24213
100.0%

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (12) 24213
100.0%

total_night_minutes
Real number (ℝ)

Distinct1853
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200.39162
Minimum0
Maximum395
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:21.939778image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117.395
Q1166.9
median200.4
Q3234.7
95-th percentile283.405
Maximum395
Range395
Interquartile range (IQR)67.8

Descriptive statistics

Standard deviation50.52778926
Coefficient of variation (CV)0.2521452207
Kurtosis0.08235919689
Mean200.39162
Median Absolute Deviation (MAD)33.8
Skewness0.01932491656
Sum1001958.1
Variance2553.057487
MonotonicityNot monotonic
2023-03-31T16:06:22.043226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (1853) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_night_calls
Real number (ℝ)

Distinct131
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99.9192
Minimum0
Maximum175
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:22.150218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile67
Q187
median100
Q3113
95-th percentile132
Maximum175
Range175
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.95868586
Coefficient of variation (CV)0.1997482552
Kurtosis0.1444380753
Mean99.9192
Median Absolute Deviation (MAD)13
Skewness0.002132842744
Sum499596
Variance398.3491412
MonotonicityNot monotonic
2023-03-31T16:06:22.248138image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (131) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_night_charge
Real number (ℝ)

Distinct1028
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.017732
Minimum0
Maximum17.77
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:22.351501image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.28
Q17.51
median9.02
Q310.56
95-th percentile12.7505
Maximum17.77
Range17.77
Interquartile range (IQR)3.05

Descriptive statistics

Standard deviation2.273762656
Coefficient of variation (CV)0.2521435163
Kurtosis0.08237761539
Mean9.017732
Median Absolute Deviation (MAD)1.52
Skewness0.01928674434
Sum45088.66
Variance5.169996615
MonotonicityNot monotonic
2023-03-31T16:06:22.452080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (1028) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_intl_minutes
Real number (ℝ)

Distinct170
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.26178
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:22.559498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.7
Q18.5
median10.3
Q312
95-th percentile14.7
Maximum20
Range20
Interquartile range (IQR)3.5

Descriptive statistics

Standard deviation2.761395715
Coefficient of variation (CV)0.2690951974
Kurtosis0.6553166102
Mean10.26178
Median Absolute Deviation (MAD)1.8
Skewness-0.2099662929
Sum51308.9
Variance7.625306293
MonotonicityNot monotonic
2023-03-31T16:06:22.661068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (170) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_intl_calls
Real number (ℝ)

Distinct21
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4352
Minimum0
Maximum20
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:22.755817image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q36
95-th percentile9
Maximum20
Range20
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.456788172
Coefficient of variation (CV)0.5539295121
Kurtosis3.268183647
Mean4.4352
Median Absolute Deviation (MAD)1
Skewness1.360692479
Sum22176
Variance6.035808122
MonotonicityNot monotonic
2023-03-31T16:06:22.833812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
Other values (21) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

total_intl_charge
Real number (ℝ)

Distinct170
Distinct (%)3.4%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.771146229
Minimum0
Maximum5.4
Zeros24
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:22.928098image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.54
Q12.3
median2.78
Q33.24
95-th percentile3.97
Maximum5.4
Range5.4
Interquartile range (IQR)0.94

Descriptive statistics

Standard deviation0.7455799771
Coefficient of variation (CV)0.2690511129
Kurtosis0.655361707
Mean2.771146229
Median Absolute Deviation (MAD)0.48
Skewness-0.2100792212
Sum13852.96
Variance0.5558895022
MonotonicityNot monotonic
2023-03-31T16:06:23.029737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
Other values (170) 4999
> 99.9%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.
Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5704
Minimum0
Maximum9
Zeros1023
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size39.2 KiB
2023-03-31T16:06:23.110574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum9
Range9
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.306363333
Coefficient of variation (CV)0.8318666153
Kurtosis1.48109554
Mean1.5704
Median Absolute Deviation (MAD)1
Skewness1.04246233
Sum7852
Variance1.706585157
MonotonicityNot monotonic
2023-03-31T16:06:23.174840image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
Other values (10) 5000
100.0%
ValueCountFrequency (%)
No values found.
ValueCountFrequency (%)
No values found.

churned
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size39.2 KiB
False.
4293 
True.
707 

Length

Max length7
Median length7
Mean length6.8586
Min length6

Characters and Unicode

Total characters34293
Distinct characters10
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row False.
2nd row False.
3rd row False.
4th row False.
5th row False.

Common Values

ValueCountFrequency (%)
Other values (2) 5000
100.0%

Length

2023-03-31T16:06:23.246569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-31T16:06:23.318692image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
false 4293
85.9%
true 707
 
14.1%

Most occurring characters

ValueCountFrequency (%)
Other values (10) 34293
100.0%

Most occurring categories

ValueCountFrequency (%)
Other values (4) 34293
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
Other values (6) 19293
100.0%
Uppercase Letter
ValueCountFrequency (%)
Other values (2) 5000
100.0%
Space Separator
ValueCountFrequency (%)
No values found.
Other Punctuation
ValueCountFrequency (%)
No values found.

Most occurring scripts

ValueCountFrequency (%)
Other values (2) 34293
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Other values (8) 24293
100.0%
Common
ValueCountFrequency (%)
Other values (2) 10000
100.0%

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

ASCII
ValueCountFrequency (%)
Other values (10) 34293
100.0%

Interactions

2023-03-31T16:06:17.820745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:01.767820image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.942272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.169781image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.377409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.649065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.825002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.038945image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.347296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.583200image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.796782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.148226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.390872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.636734image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.899997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:01.910831image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.019773image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.253942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.458299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.726472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.907406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.121423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.432421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.665024image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.879223image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.232890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.477052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.715819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.980819image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:01.987269image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.097260image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.337768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.540229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.807261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.990062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.203549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.517548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.748258image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.962604image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.317964image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.563124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.798234image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.067858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.068487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.180426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.426667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.627123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.892667image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.077132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.291326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.607539image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.836301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.050317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.407463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.653931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.883998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.152014image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.146095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.261584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.511473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.710103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.975179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.161722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.375491image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.693737image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.920997image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.135129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.493791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.740079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.966969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.234550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.224063image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.344730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.596083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.794748image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.057688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.246521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.459903image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.781507image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.007005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.220204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.580796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.827836image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.051723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.319672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.304271image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.428252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.682861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.880547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.141923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.334088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.547168image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.869880image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.095315image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.307560image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.669830image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.917349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.135859image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.404204image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.384028image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.512038image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.770066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.966149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.227591image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.422663image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.633823image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.959622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.183764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.395141image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.760194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.008083image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.219472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.492049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.466941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.598893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.859998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.053213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.315794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.513816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.723608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.050839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.274361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.485133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.852322image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.101346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.310797image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.578858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.546821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.682574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.946917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.138211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.402769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.602398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:09.920578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.140173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.363015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.572534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.942506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.191398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.395210image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.830768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.627033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.766753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.035242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.223678image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.488924image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.691730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.005002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.230402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.451753image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.660022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.033973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.281950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.480556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:18.914277image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.710420image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:03.854659image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.125057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.312253image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.578127image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.783822image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.095111image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.323280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.543908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.890235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.127780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.377059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.573368image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:19.020005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.793166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.013906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.215008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.400178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.666188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.875194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.184871image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.416473image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.634414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:13.980726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.221326image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.469133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.662340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:19.139022image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:02.868114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:04.091891image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:05.297096image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:06.480486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:07.747036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:08.958524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:10.267355image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:11.500844image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:12.716805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:14.065777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:15.306890image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:16.553989image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-03-31T16:06:17.743571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-03-31T16:06:23.387265image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
account_lengtharea_codenumber_vmail_messagestotal_day_minutestotal_day_callstotal_day_chargetotal_eve_minutestotal_eve_callstotal_night_minutestotal_night_callstotal_night_chargetotal_intl_minutestotal_intl_callstotal_intl_chargenumber_customer_service_calls
account_length1.000-0.018-0.015-0.0010.028-0.001-0.0100.0090.001-0.0080.0010.0010.0140.001-0.001
area_code-0.0181.000-0.003-0.019-0.019-0.0190.007-0.0120.0020.0150.002-0.004-0.014-0.0040.021
number_vmail_messages-0.015-0.0031.0000.0050.0010.0050.019-0.0040.0060.0030.0060.0020.0000.002-0.007
total_day_minutes-0.001-0.0190.0051.0000.0021.000-0.0110.0080.0120.0040.012-0.019-0.001-0.0200.003
total_day_calls0.028-0.0190.0010.0021.0000.002-0.0010.0040.003-0.0080.0030.0130.0110.013-0.011
total_day_charge-0.001-0.0190.0051.0000.0021.000-0.0110.0080.0120.0040.012-0.019-0.001-0.0200.003
total_eve_minutes-0.0100.0070.019-0.011-0.001-0.0111.0000.003-0.0170.013-0.0170.0000.0080.000-0.014
total_eve_calls0.009-0.012-0.0040.0080.0040.0080.0031.0000.002-0.0140.002-0.0070.006-0.0080.006
total_night_minutes0.0010.0020.0060.0120.0030.012-0.0170.0021.0000.0271.000-0.007-0.017-0.007-0.009
total_night_calls-0.0080.0150.0030.004-0.0080.0040.013-0.0140.0271.0000.0270.000-0.0000.000-0.008
total_night_charge0.0010.0020.0060.0120.0030.012-0.0170.0021.0000.0271.000-0.007-0.017-0.007-0.009
total_intl_minutes0.001-0.0040.002-0.0190.013-0.0190.000-0.007-0.0070.000-0.0071.0000.0171.000-0.012
total_intl_calls0.014-0.0140.000-0.0010.011-0.0010.0080.006-0.017-0.000-0.0170.0171.0000.017-0.019
total_intl_charge0.001-0.0040.002-0.0200.013-0.0200.000-0.008-0.0070.000-0.0071.0000.0171.000-0.012
number_customer_service_calls-0.0010.021-0.0070.003-0.0110.003-0.0140.006-0.009-0.008-0.009-0.012-0.019-0.0121.000

Missing values

2023-03-31T16:06:19.337833image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-31T16:06:19.636791image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.